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No queue for capacity
An experiment starts when the researcher is ready. Capacity is released when it finishes.
The rate at which a team learns is set by how long it waits between having an idea and seeing the result. Most of that interval is not computation. It is queueing, procurement and permission.
Used for training, evaluation, inference serving and the tooling around all three.
What this looks like in practice
An experiment starts when the researcher is ready. Capacity is released when it finishes.
The exact arrangement that produced a number is recoverable. Otherwise it is an anecdote.
Putting a model in front of users happens on the same platform as training it.
What a run cost, attributable, so the decision to repeat it is informed.
Teams optimise models and leave the loop around them untouched, when the loop is usually where the time goes.
The most valuable consequence of removing the queue is not the time saved on planned work. It is the experiments that get run at all — the speculative ones nobody would have requested capacity for, which is reliably where the surprising results come from.
Benchmarks reproducible enough to base a decision on.
A model reaching users without a platform project in between.
Preparation and versioning that keeps a result traceable to its inputs.
Review and annotation tooling built as readily as anything else.
The number of experiments we run went up by an order of magnitude. Most of the good ideas came from runs nobody would have approved in advance.
When it is ready. The queue is the thing being removed, because waiting is where most of the iteration time actually goes.
Yes, exactly — environment and inputs both. A result you cannot recreate is not a result you can build on.
No. Training and serving happen on the same platform, which removes the handover that usually sits between a working model and a used one.
Yes, attributable per run. That turns the decision to repeat an expensive experiment into an informed one.
Start on something that will still be right at a hundred times your size, and pay now what a startup should pay.
Move in stages with the old arrangement still running, and cut over only once the replacement has carried real traffic.
Keep everything at full fidelity, query it without a project, and stop deciding today what you will want to ask next year.
Ship the application your users touch, on an address that stays put, with every version one click behind you.
Build the tools your own teams need, at a cost that makes small improvements worth doing.
Give your engineers a platform that removes decisions rather than adding them, without a platform team to run it.
Begin with one workload. Keep what you have running. Decide whether to continue once you have seen it carry something real.
No migration required to begin. Nothing to tear down if you stop.